{"id":"W4308961904","doi":"10.1101/2022.11.11.516234","title":"OpenMEA: Open-Source Microelectrode Array Platform for Bioelectronic Interfacing","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; University of Toronto; University Health Network","funders":"","keywords":"Interfacing; Multielectrode array; Computer science; Neuroscience; Neuroprosthetics; Microelectrode; Deep brain stimulation; Brain stimulation; Functional electrical stimulation; Neural engineering; Stimulation; Biomedical engineering; Computer hardware; Medicine; Artificial intelligence; Disease; Biology; Parkinson's disease; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001027426,0.001268668,0.0008446847,0.001237552,0.0004360789,0.00138319,0.003246604,0.001692305,0.02790915],"category_scores_gemma":[0.002062723,0.0006223705,0.0008166848,0.0006141511,0.0004922825,0.001909163,0.002360892,0.001766758,0.01089959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004135898,"about_ca_system_score_gemma":0.0006371527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004387876,"about_ca_topic_score_gemma":0.0005731395,"domain_scores_codex":[0.998848,0.00007005769,0.00007585555,0.0001948203,0.0007103635,0.0001009187],"domain_scores_gemma":[0.9987972,0.0002709885,0.0001489291,0.0002252823,0.0004096598,0.0001478514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008573076,0.0003329522,0.001354076,0.001452921,0.0002984477,0.001610842,0.0003989654,0.008146158,0.5640108,0.02193305,0.09978919,0.2998153],"study_design_scores_gemma":[0.000199655,0.0005448319,0.003697266,0.0001864657,0.00008679909,0.001969159,0.00006908009,0.1133078,0.4360169,0.01555826,0.4279771,0.00038677],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01444991,0.001624366,0.8442709,0.000499882,0.001284584,0.0004664611,0.003690628,0.1145933,0.01911991],"genre_scores_gemma":[0.2192864,0.002282015,0.6991435,0.001258122,0.0004601392,0.002629036,0.01027417,0.01139388,0.0532726],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02790915,"threshold_uncertainty_score":0.09336543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03416012034975609,"score_gpt":0.2578733056273503,"score_spread":0.2237131852775943,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}